Lane-level map matching

ABSTRACT

The present disclosure relates to lane-level map matching for a vehicle. The method includes receiving vehicle data including a geographical position of the vehicle, a heading of the vehicle, and a speed of the vehicle and receiving sensor data from a perception system of the vehicle. The sensor data includes information about a position of at least one road reference in a surrounding environment of the vehicle. Operations include receiving map data including a lane geometry of the surrounding environment of the vehicle, the lane geometry including a set of candidate lanes. Operations include forming a state space model including a set of states. Each state of the set of states represents a candidate lane of the set of candidate lanes, and defining a cost for going from each state to every other state of the set of states based on the received vehicle data and the received sensor data.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application for patent claims priority to European PatentOffice Application Ser. No. 19179083.1, entitled “LANE-LEVEL MAPMATCHING” filed on Jun. 7, 2019, assigned to the assignee hereof, andexpressly incorporated herein by reference.

TECHNICAL FIELD OF THE INVENTION

The present disclosure generally relates to a procedure of matching avehicle's geographical location data to digital map locations, i.e. mapmatching. More specifically, the present disclosure relates to asolution for determining a lane path of an autonomous vehicle (AV) orsemi-autonomous vehicle (i.e. vehicle equipped with advanceddriver-assistance systems).

BACKGROUND

Today's maps are mainly designed for human use. More specifically, theyare intended to be used for turn-by-turn navigation purposes. Otherenvironmental information, such as the type and location of lanemarkers, any debris lying on the road and road maintenance obstructions,is visually obtained by the map user as he/she navigates through theroads. Autonomous vehicles, however, require very different maps. Morespecifically, these maps need to be in high-definition (HD), providingthe “robots” with very precise localization and the possibility toperceive the environment. The HD maps will also need to be updatedcontinuously, to track events such as road accidents or trafficcongestion.

Furthermore, just as the vehicle needs minute information about itsenvironment, it needs to know its position on the road. This problem ofidentifying one's position on the road, is called map matching, and canmore formally be described as the procedure of matching location data toa digital map in order to obtain the true position in a road network.Map matching can be considered to be an important aspect for navigationand route guidance systems.

Existing solutions, built on e.g. probability theory, fuzzy logic theoryand belief theory, are used to map GPS coordinates to a certain roadsegment in order to give information about the surroundings of avehicle.

However, there is a need for new and improved solutions which providemore accurate and robust map matching, by e.g. compensating for thenoise nature of GPS signals.

SUMMARY OF THE INVENTION

It is therefore an object of the present disclosure to provide a methodfor lane-level map matching for a vehicle, a computer-readable storagemedium, a control device, and a vehicle which alleviate all or at leastsome of the drawbacks of presently known systems.

This object is achieved by means of a method for lane-level map matchingfor a vehicle, a computer-readable storage medium, a control device, anda vehicle as defined in the appended claims. The term exemplary is inthe present context to be understood as serving as an instance, exampleor illustration.

According to a first aspect of the present disclosure, there is provideda method for lane-level map matching for a vehicle. The method comprisesreceiving vehicle data comprising a geographical position of thevehicle, a heading of the vehicle, and a speed of the vehicle. Themethod further comprises receiving sensor data from a perception systemof the vehicle. The sensor data comprising information about a positionof at least one road reference in a surrounding environment of thevehicle. Furthermore the method comprises receiving map data comprisinga lane geometry of the surrounding environment of the vehicle, the lanegeometry comprising a set of candidate lanes. Next, the method comprisesforming a state space model comprising a set of states, wherein eachstate of the set of states represents a candidate lane of the set ofcandidate lanes, and defining a cost for going from each state to everyother state of the set of states based on the received vehicle data andthe received sensor data. Furthermore, the method comprises determininga probable path for the vehicle based on the formed state space modeland the defined costs.

The presented method enables for reliable and accurate lane-level mapmatching. Moreover, the proposed method is robust to error-prone datasuch as noise GPS measurements and imperfect vision sensor detections.Moreover, the method can be implemented as an online algorithm, whichallows for global optimality in every stage of the calculation.

Further, the present inventors realized that sensor observations of thesurrounding environment can be fused together with location data inorder to add robustness to conventional GPS-based map matching.Moreover, by forming a state space model and computing the most probablepath by observing the probability of the vehicle being in a particularstate at a given time, a computationally efficient and accurate mapmatching solution can be provided.

According to an exemplary embodiment of the present disclosure eachstate is a hidden state in a Hidden Markov Model, and the cost isdefined based on a first predefined probability E_(k) for making anobservation y_(k) at a time t_(k) when being in state x_(m), and asecond predefined probability T_(k) for moving from a first state x_(k)of the set of states to another state x_(k+1) of the set of states atthe time t_(k).

According to a second aspect of the present disclosure, there isprovided a (non-transitory) computer-readable storage medium storing oneor more programs configured to be executed by one or more processors ofa vehicle control system, the one or more programs comprisinginstructions for performing the method according to any one of theembodiments disclosed herein. With this aspect of the disclosure,similar advantages and preferred features are present as in thepreviously discussed first aspect of the disclosure.

The term “non-transitory,” as used herein, is intended to describe acomputer-readable storage medium (or “memory”) excluding propagatingelectromagnetic signals, but are not intended to otherwise limit thetype of physical computer-readable storage device that is encompassed bythe phrase computer-readable medium or memory. For instance, the terms“non-transitory computer readable medium” or “tangible memory” areintended to encompass types of storage devices that do not necessarilystore information permanently, including for example, random accessmemory (RAM). Program instructions and data stored on a tangiblecomputer-accessible storage medium in non-transitory form may further betransmitted by transmission media or signals such as electrical,electromagnetic, or digital signals, which may be conveyed via acommunication medium such as a network and/or a wireless link. Thus, theterm “non-transitory”, as used herein, is a limitation of the mediumitself (i.e., tangible, not a signal) as opposed to a limitation on datastorage persistency (e.g., RAM vs. ROM).

According to a third aspect of the present disclosure there is provideda control device for lane-level map matching for a vehicle, where thecontrol device comprises control circuitry configured to receive vehicledata comprising a geographical position of the vehicle, a heading of thevehicle, and a speed of the vehicle. The control circuitry is configuredto receive sensor data from a perception system of the vehicle. Thesensor data comprises information about a position of at least one roadreference in a surrounding environment of the vehicle. Further, thecontrol circuitry is configured to receive map data comprising a lanegeometry of the surrounding environment of the vehicle, where the lanegeometry comprises a set of candidate lanes. Next, the control circuitryis configured to form a state space model comprising a set of states,wherein each state of the set of states represents a candidate lane ofthe set of candidate lanes. The control circuitry is further configuredto define a cost for going from each state to every other state of theset of states based on the received vehicle data and the received sensordata, and determine a probable path for the vehicle based on the formedstate space model and the defined costs. With this aspect of thedisclosure, similar advantages and preferred features are present as inthe previously discussed first aspect of the disclosure.

According to a fourth aspect of the present disclosure, there isprovided a vehicle comprising a perception system comprising at leastone sensor for monitoring a surrounding environment of the vehicle. Thevehicle further comprises an inertial measurement unit (IMU) formeasuring an inertial movement of the vehicle, a localization system formonitoring a geographical position and a heading of the vehicle, and acontrol device according to any one of the embodiments disclosed herein.With this aspect of the disclosure, similar advantages and preferredfeatures are present as in the previously discussed first aspect of thedisclosure.

Further embodiments of the invention are defined in the dependentclaims. It should be emphasized that the term “comprises/comprising”when used in this specification is taken to specify the presence ofstated features, integers, steps, or components. It does not precludethe presence or addition of one or more other features, integers, steps,components, or groups thereof.

These and other features and advantages of the present invention will inthe following be further clarified with reference to the embodimentsdescribed hereinafter.

BRIEF DESCRIPTION OF FIGURES

Further objects, features and advantages of embodiments of thedisclosure will appear from the following detailed description,reference being made to the accompanying drawings, in which:

FIG. 1 is a schematic flow chart representation of a method forlane-level map matching for a vehicle traveling on a road in accordancewith an embodiment of the present disclosure.

FIG. 2 is a schematic graphical representation of a discrete HiddenMarkov Model (HMM) having three states and three observations withcorresponding transmission and emission probabilities in accordance withan embodiment of the present disclosure.

FIG. 3 is a schematic trellis diagram representation of the HMM of FIG.2 .

FIG. 4 is a schematic side view of a vehicle having a control device forlane-level map matching for a vehicle traveling on a road in accordancewith an embodiment of the present disclosure.

DETAILED DESCRIPTION

Those skilled in the art will appreciate that the steps, services andfunctions explained herein may be implemented using individual hardwarecircuitry, using software functioning in conjunction with a programmedmicroprocessor or general purpose computer, using one or moreApplication Specific Integrated Circuits (ASICs) and/or using one ormore Digital Signal Processors (DSPs). It will also be appreciated thatwhen the present disclosure is described in terms of a method, it mayalso be embodied in one or more processors and one or more memoriescoupled to the one or more processors, wherein the one or more memoriesstore one or more programs that perform the steps, services andfunctions disclosed herein when executed by the one or more processors.

In the following description of exemplary embodiments, the samereference numerals denote the same or similar components. A vehicle isin the present context to be understood as a road vehicle such as a car,a bus, a truck, construction vehicles, and the like.

FIG. 1 is a schematic flow chart representation of a method 100 forlane-level map matching for a vehicle. Map matching can be understood asa process of matching a vehicle's location data to a digital map inorder to obtain the true position in a road network. FIG. 1 furtherincludes illustrative drawings of the different method steps 101-106 tothe right of the boxes 101-106 of the flow chart. Moreover, the method100 provides for “lane-level” matching, meaning that not only is thevehicle location data matched to a specific road, but to a specific laneon that road.

Moving on, the method 100 comprises receiving vehicle data from e.g. alocalization system of the vehicle. The vehicle data comprises ageographical position (latitude, longitude), a heading of the vehicle(yaw angle) and a speed of the vehicle. The vehicle data may be received101 from a localization system of the vehicle (e.g. a Global NavigationSatellite System, GNSS), an inertial measurement unit (IMU) togetherwith HD Map data, or a combination of system. The method 100 furthercomprises receiving sensor data from a perception system of the vehicle.The sensor data comprises at least information about a position of atleast one road reference in a surrounding environment of the vehicle. Aroad reference may for example be a lane marking, a traffic sign, a roadedge, a road barrier, or any other suitable landmark. Moreover, theposition of the one or more road references can be determined inreference to a local coordinate system of the vehicle or in reference toa global coordinate system, depending on application and implementationchoices. A perception system is in the present context to be understoodas a system responsible for acquiring raw sensor data from on sensorssuch as cameras, LIDARs and RADARs, ultrasonic sensors, and convertingthis raw data into scene understanding. Naturally, the sensor data maybe received 102 directly from one or more suitable sensors (such as e.g.cameras, LIDAR sensors, radars, ultrasonic sensors, etc.)

Further, in relation to the road references, the sensor data maycomprise lane marker data, where the lane marker data can include adistance to one or more lane markers (relative to the ego-vehicle) and atype of lane marker (broken line, semantics, solid line, etc.). Sensordata may further include lane marking geometries (could be representedas a polynomial or as a clothoid, depending on camera software).

The method 100 further comprises receiving 103 map data comprising alane geometry of the surrounding environment of the vehicle. The lanegeometry comprises a set (i.e. a plurality) of candidate lanes. Acandidate lane is in the present context to be understood as a drivablelane on a road in the surrounding environment of the vehicle, i.e. alane in which the vehicle may or may not travel. The map data may be inthe form of High-Definition (HD) map data, i.e. map data having highprecision (at centimetre-level). HD maps are maps that are purposelybuilt for robots to manoeuvre themselves around a 3D space. In moredetail, these maps need to be precise and contain a lot of information,which humans may take for granted. Not only that the maps should containinformation about where the lanes are, where the road boundaries are,one also wants to know where the curves are and how high the curves are.

The lane geometry may for example be retrieved 103 from defined portionof an HD map, where the defined portion may be all lane geometries thatare within a circle of a predefined radius. The centre of the circle mayfor example be determined based on the previously received 101geographical position of the vehicle. The radius may be defined based ona measurement error of the received 101 geographical position, forexample 20 meters, or 50 meters in an urban canyon. In order to increasecomputing efficiency, the method may include forming a spatial data basebased on the received 103 map data and a spatial indexing method (e.g.Geohash, Quadtree, M-tree, X-tree, R-tree, etc.). For example, an R-treeof the retrieved 103 lane geometries can be built and furthermore packedby using a sorting method (e.g. Sort-Tile-Recursive (STR), Nearest-X,Overlap Minimizing Top-Down (OMT), etc.). This spatial indexing andpacking allows one to quickly find a set of candidate lanes from thereceived 103 HD map.

Next, a state space model comprising a set of states is formed 104. Eachstate of the set of states represents a candidate lane of the set ofcandidate lanes (retrieved from the map data). A state space model is inthe present context to be understood as type of probabilistic graphicalmodel, which describes a probabilistic dependence between the latentstate variable and the observed measurement. The state or themeasurement can be either continuous or discrete. The state space modelis used to provide a general framework for analysing deterministic andstochastic dynamical systems that are measured or observed through astochastic process.

More specifically, the method 100 may comprise forming a Hidden MarkovModel (HMM), which is considered to be a sub-type of state-space modelin the present disclosure. In more detail, each candidate lane isconsidered to be represented by a hidden state in the HMM, where thereceived vehicle data and sensor data are used to estimate the hiddenstates, i.e. in which lanes the vehicle is actually traveling. Furtherdetails related to the Hidden Markov Model and embodiments of thepresent disclosure related thereto will be discussed in reference toFIG. 2 .

Further, the method 100 comprises defining 105 a cost for going fromeach state to every other state of the set of states in the state spacemodel. The costs are computed based on the received vehicle data, thereceived sensor data and the received map data. In more detail, thecosts can be defined 105 as probabilities, i.e. the likelihood of thevehicle going from a first state to a second state at a time t orremaining in the first state at the time t.

Moving on, the method further comprises determining 106 a probable pathfor the vehicle based on the formed state space model and the definedcosts. In other words, the probable path of the vehicle is determined bycomputing the probability of a plurality of possible paths (statetransitions) where the probability is determined based on the received102 sensor observations and the received 101 vehicle data. Thecomputation for determining 106 the probable path may for example beexecuted by numerical optimization solvers.

Initial state probabilities, i.e. the probability of the vehicle beingin each state at time t=0 may also be computed. For this computation onemay use the received 101 vehicle data, the received 102 sensor data, andthe obtained 103 HD map. Alternatively, one may assign equalprobabilities for starting in each state.

Moreover, the method 100 may further comprise a step of sending thedetermined 106 probable path to a control system for controlling adriver-assistance or autonomous driving feature of the vehicle, or to amap generating system for updating a map of the surrounding environment.In reference to the former, the probable path may be used to activate,deactivate or adjust specific ADAS or AD features of the vehicle basedon which lane the vehicle is traveling. In more detail, the probablepath may be used to more accurately estimate a position of the vehicle,wherefore appropriate adjustments of ADAS or AD features may beperformed (e.g. adjusting a following distance to lead vehicle,adjusting emergency brake thresholds, etc.). In reference to the mapgenerating system, which may be local or remote the vehicle, theprobable path can be used as input to identify new lanes or re-routedlanes. Thus, vehicle's implementing the disclosed method can be used asprobes for map updates.

Referring to FIG. 1 , a stretch of road having three lanes x1, x2, x3 isdepicted. A first lane x1 and a second lane x2 are parallel and separateby double lane markings. The second lane x2 splits into a third lane x3.In the schematic drawings, the corresponding state transitions areillustrated as circles interconnected with solid arrows. In theillustrated case of FIG. 1 , there are only two possible lane candidatesat time x1 and x2 at time t−1. In other words, at time t−1, the vehiclecan only travel in one of two possible lanes (retrieved from thereceived 103 map data with the help of the received 101 vehicle datacomprising a GPS position with an associated measurement error radius asdescribed in the foregoing).

The arrows indicate the possible transitions between the lanes x₁, x₂,x₃. For example, given that the vehicle is in lane x₁ at time t−1, itcan either stay in lane x₁, or it can change lane from x₁ to x₂. Sincethe double lane marking is solid/dashed, one can argue conclude thatthere is a relatively small probability that the vehicle changes lanefrom the first lane x₁ to the second lane x₂ (since a lane change fromthe first lane x₁ to the second lane x₂ would imply that the vehicle isbreaking a traffic rule, which is here assumed to have a reducedlikelihood). On the other hand, the vehicle is more likely to changelane from x₂ to x₁ (perhaps there is a vehicle in front of it that slowsdown in order to turn onto the exit lane x₃). Of course, the car canalso stay in its lane. Moreover, the topology of the road networkrestricts us from going directly from the first lane x₁ to the thirdlane x₃ (without first passing the second lane x₂), in the presentexample. Further, the probable path can be determined 106 by computingthe costs, at time t>0, of going from any candidate lane at time t−1 toany other candidate state at time t. For this computation, measurementscontained in the received 101 vehicle data and the received 102 sensordata as well as the received 103 map data are used. The costs may forexample be determined/computed 105 by multiplying probabilities such asP(y_(t−1)|x₁ ^(t−1))*P(x_(t)|x₁ ^(t−1)), where P(y_(t−1)|x₁ ^(t−1)) canbe defined as a difference between the lane marking type reported by theperception system and the candidate HD map lane marking type, whileP(x_(t)|x₁ ^(t−1)) can be defined as a difference between the measuredheading of the vehicle and a direction of a candidate lane in the HDmap. An optimization algorithm (e.g. a max-sum or max-product algorithm)can then be employed to determine 106 the most probable path of thevehicle.

As previously mentioned, the candidate lanes may be represented ashidden states in a Hidden Markov Model (HMM). Thus, FIG. 2 shows aschematic graphical representation of a discrete HMM with three statess₁, s₂, s₃ and three observations y₁, y₂, y₃ with correspondingtransmission probabilities T_(ij), i, j∈{1, 2, 3} and emissionprobabilities E_(k,i,)k∈{1, 2, 3}, i∈{1, 2, 3}.

A Hidden Markov Model (HMM) can be construed as a statistical model inwhich the system being modelled is assumed to be a stochastic processwith unobserved, i.e. hidden, states. The states are contained in a set

, while χ⊆

describe states in a sequence. More explicitly, the set of states mightbe

={s₁, s₂} and a state sequence x could look like x=[x₁, x₂, x₃]=[s₂, s₂,s₁], where x₁, x₂, x₃∈⊆, and s₁, s₂∈

. The states of an HMM are not directly visible to the observer, whichis why they are called hidden. Rather, there exists observations

that stem from these hidden states. The sequence of observations aregenerated by a second stochastic process. In that sense, an HMM is adoubly stochastic process. The HMM comprises three main parameterstransition probabilities, emission probabilities and initial statedistribution. As a sub-category of Markov Models, the HMM also satisfiesthe Markov property.

In FIG. 2 , each hidden state 21, 22, 23 represent a candidate laneobtained from the lane geometry in the received map data, and the bottomboxes 31, 32, 34, 34 represent different “observations” e.g. position oflane markings, type of lane markings, heading, geographical position,etc. Thus, the received vehicle data and sensor data form an observationy_(k) for a time t_(k).

The cost for going from each state 21, 22, 23 to every other state 21,22, 23 of the set of states is based on the received vehicle data andthe received sensor data. The costs may for example be defined based ontwo probabilities, namely a first predefined probability E_(k) and asecond predefined probability T_(k). In accordance with the exemplaryembodiment in which the state space model is in the form of a HiddenMarkov Model, the first and second predefined probabilities may bereferred to as an emission probability and a transmission probability,respectively. The Emission Probability E_(k) defines the probability formaking an observation y_(k) at a time t_(k), when being in a statex_(k). The transmission probability T_(k) defines a probability formoving from a first state x_(k) of the set of states to another statex_(k+1) of the set of states at the time t_(k).

In more detail, the emission probability E_(k) is associated with thesecond stochastic process, which models the distribution ofobservations. Each observation y_(k) has an emission probability,E _(k) =P(y _(k) |x _(k))x _(k)∈χ  (1)which is a probability distribution function that, as mentioned,reflects the probability of making an observation y_(k) at a time t_(k),when being in a state x_(k). When considering explicit states, theemission probability is more appropriately given as,E _(k,i) =P(y _(k) |x _(k) =s _(i))s _(i)∈

.  (2)

The transmission probability T_(k) is as mentioned, the likelihood ofmoving from a first state x_(k) of the set of states to another statex_(k+1) at the time t_(k). It can be written as,T _(k) =P(x _(k+1) |x _(k))x _(k) ,x _(k+1)∈χ.  (3)

When considering explicit states, the transition probability is moreappropriately given as,T _(i,j) =P(x _(k+1) =s _(j) |x _(k) =s _(i))s _(i) ,s _(j)∈

.

The distribution of initial state probabilities describes the likelihoodof starting in each state,Π_(i) =P(x ₀ =s _(i))s _(i)∈

.  (5)

According to an exemplary embodiment of the present disclosure, thesensor data comprises lane marker data. The lane marker data comprisesat least a distance to the at least one lane marker and a lane markertype of the at least one lane marker. Moreover, the vehicle data furthercomprises a yaw rate of the vehicle (obtained from e.g. an inertialmeasurement unit (IMU) of the vehicle or a steering wheel angle sensor).Accordingly, the first predefined probability (emission probability)E_(k) may be based one or more of the distance to the at least one lanemarker, the lane marker type of the at least one lane marker, thegeographical position of the vehicle, and the speed of the vehicle. Theemission probability E_(k) may naturally be based on further parameterssuch as e.g. the position and type of other landmarks (e.g. trafficsigns), lane text (e.g. bus lane), a position of other vehicles in thesurrounding environment of the vehicle, confidence of left/right lanemarker, distance between left and right marker, and so forth. The secondpredefined probability (transmission probability) T_(k) may on the otherhand be based on the yaw rate of the vehicle.

In more detail, the yaw rate may be used a lane change indicator sincethe steering angle approximately resembles a sine wave over time duringa lane change. This wave can be modelled by fitting a parametrized sinefunction s(ω) of the yaw rate ω,s(ω)=α·sin(fω+p)+c  (6)Yaw rates corresponding to a left lane change have an opposite sign ascompared to right lane changes. Naturally, the transmission probabilitymay be further based on other lane change indicators in order to improveredundancy. For example, visual data obtained from a vehicle perceptionsystem can be used to estimate when a lane change occurs (e.g. based onlane marker types, distance to lane markers, change of distance to lanemarkers, etc.). Moreover, lane tracing models (e.g. expressed aspolynomials or clothoids) can be used as lane change indicators. In moredetail, the lateral offset parameter (often denoted as a₀ for polynomiallane boundary representations) can be used to indicate a lane change.For example, if the lateral offset parameter (lateral offset between thevehicle and the lane trace) of the right lane trace becomes smaller andsmaller over time, it is probable that a right lane change is occurring.

A discrete HMM may be represented as a trellis diagram, where time stepst₁-t₄ are incorporated, as illustrated in FIG. 3 . Each node 30 a-30 lin the diagram corresponds to a distinct state s₁-s₃ at a given timet₁-t₄, and the edges (interconnecting lines) represent possibletransitions to states at the next time step t₁-t₄. The edge weights arehere chosen to correspond to the product of transition and emissionprobabilities T_(k), E_(k). In the illustrated example, some of theseproducts are zero wherefore the edges have been removed to avoidcluttering. A useful property of this particular representation is thatevery possible state sequence in the model corresponds to a unique paththrough the trellis, and the other way around. Because of this, it is auseful representation when applying dynamic programming algorithms (e.g.Viterbi algorithm) to an HMM for finding the most probable path throughthe model.

In more detail, it can be said that inferring which sequence of statesthat caused a specific sequence of observations is called decoding. TheViterbi algorithm, is one example of such a decoder. More specifically,it can be said that the Viterbi algorithm solves the problem ofestimating the maximum likelihood of state sequences, i.e. it finds themost probable state sequence. The set of transition sequences can bedefined as ξ={ξ₁, . . . , ξ_(K−1)} with the transitions ξ_(k)={x_(k+1),x_(k)} at the given time k. These map one-to-one to the state sequencex={x₁, . . . , x_(K)}. Using this notation the observations discussed inreference to FIG. 2 y={y₁, . . . , y_(K)}, y_(k)∈

, can be described as the output of a channel, whose input is thetransition sequences. The channel is memory-less in the sense thatP(y|ξ)=Π_(k=0) ^(K)P(y_(k)|ξ_(k)), i.e. each observation only dependsprobabilistically on the transition ξ_(k).

Executable instructions for performing these functions are, optionally,included in a non-transitory computer-readable storage medium or othercomputer program product configured for execution by one or moreprocessors.

FIG. 4 is a schematic side view of a vehicle 1 comprising a controldevice 10 for lane-level map matching. The vehicle 1 further comprises aperception system 6, an inertial measurement unit (IMU) 7, and alocalization system 5. A perception system 6 is in the present contextto be understood as a system responsible for acquiring raw sensor datafrom on sensors 6 a, 6 b, 6 c such as cameras, LIDARs and RADARs,ultrasonic sensors, and converting this raw data into sceneunderstanding. The localization system 5 is configured to monitor ageographical position and heading of the vehicle, and may in the form ofa Global Navigation Satellite System (GNSS), such as a GPS. However, thelocalization system may alternatively be realized as a Real TimeKinematics (RTK) GPS in order to improve accuracy. An IMU 7 is to beunderstood as an electronic device configured to measure the inertialmovement of the vehicle 1. An IMU 7 usually has six degrees of freedom,three accelerometers and three gyroscopes.

The control device 10 comprises one or more processors 11, a memory 12,a sensor interface 13 and a communication interface 14. The processor(s)11 may also be referred to as a control circuit 11 or control circuitry11. The control circuit 11 is configured to execute instructions storedin the memory 12 to perform a method for lane-level map matching for avehicle according to any one of the embodiments disclosed herein. Stateddifferently, the memory 12 of the control device 10 can include one ormore (non-transitory) computer-readable storage mediums, for storingcomputer-executable instructions, which, when executed by one or morecomputer processors 11, for example, can cause the computer processors11 to perform the techniques described herein. The memory 12 optionallyincludes high-speed random access memory, such as DRAM, SRAM, DDR RAM,or other random access solid-state memory devices; and optionallyincludes non-volatile memory, such as one or more magnetic disk storagedevices, optical disk storage devices, flash memory devices, or othernon-volatile solid-state storage devices.

In more detail, the control circuitry 11 is configured to receivevehicle data comprising a geographical position of the vehicle, aheading of the vehicle and a speed of the vehicle. The vehicle data mayfor example be obtained from a GPS unit of the vehicle 1. The controlcircuitry 11 is further configured to receive sensor data from aperception system 6 of the vehicle 1. The sensor data comprisesinformation about a position of at least one road reference in asurrounding environment of the vehicle. The position of the roadreference may either be in reference to the vehicle or in a “global”coordinate system depending on specifications. In more detail, theperception system 6 preferably a forward looking camera 6 c configuredto detect lane-markings on a road. Conventional automotive grade camerasare capable of detecting lane markers that lie within a 25 meter range.The detections give information about distances to the closest markingson the left and right side of the vehicle and their corresponding type.The marker types that can be recognized by the system 6 or the camera 6c include e.g. solid and dashed. Once the lane-markings have beendetected, the perceptive projection image can be transformed into itscorresponding bird's eye vision.

The control circuitry 11 is further configured to receive map datacomprising a lane geometry of the surrounding environment of thevehicle. The lane geometry comprises a set of candidate lanes. The mapdata may be in the form of a HD map comprising information about roadshaving multiple parallel lanes, each of which has a centre linerepresented as a polyline. The polylines are generally two-dimensional,defined by the longitude and latitude of the start and end of each linesegment. Also, left and right lane markers signifying the lane border,are polylines. They are also associated with their marker type (e.g.dashed or solid). The map data may also comprise road delimiters such asguard rails, speed limits, lane orientation, etc.

Further, the control circuitry 11 is configured to form a state spacemodel comprising a set of states. Each state of the set of statesrepresents a candidate lane of the set of candidate lanes. Next, thecontrol circuitry 11 is configured to define a cost for going from eachstate to every other state of the set of states based on the receivedvehicle data, the received sensor data and the received map data.Various implementations for computing the costs have already beendiscussed in detail in the foregoing and are analogously applicable withthis aspect of the disclosure.

Still further, the control circuitry 11 is configured to determine aprobable path for the vehicle based on the formed state space model andthe defined costs. In other words, the control circuitry 11 isconfigured to calculate the most probable path that the vehicle 1 hastravelled in based on the formed state space model and defined costs formoving between the states (i.e. moving between the lanes). Even thoughthe control device 11 is here illustrated as an in-vehicle system, someor all of the components may be located remote (e.g. cloud-basedsolution) to the vehicle in order to increase computational power.

Further, the vehicle 1 may be connected to external network(s) 2 via forinstance a wireless link (e.g. for retrieving map data). The same orsome other wireless link may be used to communicate with other vehicles2 in the vicinity of the vehicle or with local infrastructure elements.Cellular communication technologies may be used for long rangecommunication such as to external networks and if the cellularcommunication technology used have low latency it may also be used forcommunication between vehicles, vehicle to vehicle (V2V), and/or vehicleto infrastructure, V2X. Examples of cellular radio technologies are GSM,GPRS, EDGE, LTE, 5G, 5G NR, and so on, also including future cellularsolutions. However, in some solutions mid to short range communicationtechnologies are used such as Wireless Local Area (LAN), e.g. IEEE802.11 based solutions. ETSI is working on cellular standards forvehicle communication and for instance 5G is considered as a suitablesolution due to the low latency and efficient handling of highbandwidths and communication channels.

The present disclosure has been presented above with reference tospecific embodiments. However, other embodiments than the abovedescribed are possible and within the scope of the disclosure. Differentmethod steps than those described above, performing the method byhardware or software, may be provided within the scope of thedisclosure. Thus, according to an exemplary embodiment, there isprovided a non-transitory computer-readable storage medium storing oneor more programs configured to be executed by one or more processors ofa vehicle control system, the one or more programs comprisinginstructions for performing the method according to any one of theabove-discussed embodiments. Alternatively, according to anotherexemplary embodiment a cloud computing system can be configured toperform any of the methods presented herein. The cloud computing systemmay comprise distributed cloud computing resources that jointly performthe methods presented herein under control of one or more computerprogram products.

Generally speaking, a computer-accessible medium may include anytangible or non-transitory storage media or memory media such aselectronic, magnetic, or optical media—e.g., disk or CD/DVD-ROM coupledto computer system via bus. The terms “tangible” and “non-transitory,”as used herein, are intended to describe a computer-readable storagemedium (or “memory”) excluding propagating electromagnetic signals, butare not intended to otherwise limit the type of physicalcomputer-readable storage device that is encompassed by the phrasecomputer-readable medium or memory. For instance, the terms“non-transitory computer-readable medium” or “tangible memory” areintended to encompass types of storage devices that do not necessarilystore information permanently, including for example, random accessmemory (RAM). Program instructions and data stored on a tangiblecomputer-accessible storage medium in non-transitory form may further betransmitted by transmission media or signals such as electrical,electromagnetic, or digital signals, which may be conveyed via acommunication medium such as a network and/or a wireless link.

The processor(s) 11 (associated with the control device 10) may be orinclude any number of hardware components for conducting data or signalprocessing or for executing computer code stored in memory 12. Thedevice 10 has an associated memory 12, and the memory 12 may be one ormore devices for storing data and/or computer code for completing orfacilitating the various methods described in the present description.The memory may include volatile memory or non-volatile memory. Thememory 12 may include database components, object code components,script components, or any other type of information structure forsupporting the various activities of the present description. Accordingto an exemplary embodiment, any distributed or local memory device maybe utilized with the systems and methods of this description. Accordingto an exemplary embodiment the memory 12 is communicably connected tothe processor 11 (e.g., via a circuit or any other wired, wireless, ornetwork connection) and includes computer code for executing one or moreprocesses described herein.

It should be appreciated that the sensor interface 13 may also providethe possibility to acquire sensor data directly or via dedicated sensorcontrol circuitry 6 in the vehicle. The communication/antenna interface14 may further provide the possibility to send output to a remotelocation (e.g. remote operator or control centre) by means of theantenna 8. Moreover, some sensors in the vehicle may communicate withthe control device 10 using a local network setup, such as CAN bus, I2C,Ethernet, optical fibres, and so on. The communication interface 14 maybe arranged to communicate with other control functions of the vehicleand may thus be seen as control interface also; however, a separatecontrol interface (not shown) may be provided. Local communicationwithin the vehicle may also be of a wireless type with protocols such asWiFi, LoRa, Zigbee, Bluetooth, or similar mid/short range technologies.

Accordingly, it should be understood that parts of the describedsolution may be implemented either in the vehicle, in a system locatedexternal the vehicle, or in a combination of internal and external thevehicle; for instance in a server in communication with the vehicle, aso called cloud solution. For instance, sensor data may be sent to anexternal system and that system performs the steps to defining the costsfor going from one state to the other. The different features and stepsof the embodiments may be combined in other combinations than thosedescribed.

It should be noted that the word “comprising” does not exclude thepresence of other elements or steps than those listed and the words “a”or “an” preceding an element do not exclude the presence of a pluralityof such elements. It should further be noted that any reference signs donot limit the scope of the claims, that the invention may be at least inpart implemented by means of both hardware and software, and thatseveral “means” or “units” may be represented by the same item ofhardware.

Although the figures may show a specific order of method steps, theorder of the steps may differ from what is depicted. In addition, two ormore steps may be performed concurrently or with partial concurrence.For example, the steps of receiving signals comprising information abouta movement and information about a current road scenario may beinterchanged based on a specific realization. Such variation will dependon the software and hardware systems chosen and on designer choice. Allsuch variations are within the scope of the disclosure. Likewise,software implementations could be accomplished with standard programmingtechniques with rule-based logic and other logic to accomplish thevarious connection steps, processing steps, comparison steps anddecision steps. The above mentioned and described embodiments are onlygiven as examples and should not be limiting to the present invention.Other solutions, uses, objectives, and functions within the scope of theinvention as claimed in the below described patent embodiments should beapparent for the person skilled in the art.

What is claimed is:
 1. A method for lane-level map matching for avehicle, the method comprising: receiving vehicle data comprising ageographical position of the vehicle, a heading of the vehicle, and aspeed of the vehicle; receiving sensor data from a perception system ofthe vehicle, the sensor data comprising information about a position ofat least one road reference in a surrounding environment of the vehicle;receiving high-definition (HD) map data comprising a lane geometry ofthe surrounding environment of the vehicle, the lane geometry comprisinga set of candidate lanes; forming a state space model comprising a setof states, wherein each state of the set of states represents acandidate lane of the set of candidate lanes; defining a cost for goingfrom each state to every other state of the set of states based on thereceived vehicle data, the received sensor data and the received HD mapdata; and determining a probable path of vehicle travel based on theformed state space model and the defined costs, wherein the determiningthe probable path comprises decoding the formed state space model inorder to estimate the maximum likelihood of a plurality of statesequences and determine the most probable state sequence; and sendingthe probable path to a map generating system for updating an HD map ofthe surrounding environment.
 2. The method according to claim 1, whereineach state is a hidden state in a Hidden Markov Model, and wherein thecost is defined based on: a first predefined probability E_(k) formaking an observation y_(k) at a time t_(k) when being in state x_(m);and a second predefined probability T_(k) for moving from a first statex_(k) of the set of states to another state x_(k+1) of the set of statesat the time t_(k).
 3. The method according to claim 2, wherein thesensor data comprises lane marker data comprising a distance to at leastone lane marker and a lane marker type of the at least one lane marker,wherein the vehicle data further comprises a yaw rate of the vehicle,wherein the first predefined probability E_(k) is based on at least oneof the distance to the at least one lane marker, the lane marker type ofthe at least one lane marker, the geographical position of the vehicle,and the speed of the vehicle, and wherein the second predefinedprobability T_(k) is based on the yaw rate of the vehicle.
 4. The methodaccording to claim 1, wherein the sensor data comprises lane marker datacomprising a distance to the at least one lane marker and a type of theat least one lane marker.
 5. The method according to claim 1, whereinthe vehicle data further comprises a yaw rate of the vehicle.
 6. Themethod according to claim 2, further comprising: forming a spatialdatabase based on the HD map data and a spatial indexing method.
 7. Themethod according to claim 1, further comprising: sending the probablepath to a control system for controlling a driver-assistance orautonomous driving feature of the vehicle based on the probable path. 8.A non-transitory computer-readable storage medium storing one or morecomputer readable codes which, when executed by one or more processorsof a vehicle control system, the one or more computer readable codescomprising instructions for performing the method comprising: receivingvehicle data comprising a geographical position of the vehicle, aheading of the vehicle, and a speed of the vehicle; receiving sensordata from a perception system of the vehicle, the sensor data comprisinginformation about a position of at least one road reference in asurrounding environment of the vehicle; receiving high-definition (HD)map data comprising a lane geometry of the surrounding environment ofthe vehicle, the lane geometry comprising a set of candidate lanes;forming a state space model comprising a set of states, wherein eachstate of the set of states represents a candidate lane of the set ofcandidate lanes; defining a cost for going from each state to everyother state of the set of states based on the received vehicle data, thereceived sensor data and the received HD map data; and determining aprobable path of vehicle travel based on the formed state space modeland the defined costs, wherein the determining the probable pathcomprises decoding the formed state space model in order to estimate themaximum likelihood of a plurality of state sequences and determine themost probable state sequence; and sending the probable path to a mapgenerating system for updating an HD map of the surrounding environment.9. A control device for lane-level map matching for a vehicle, thecontrol device comprising control circuitry configured to: receivevehicle data comprising a geographical position of the vehicle, aheading of the vehicle, and a speed of the vehicle; receive sensor datafrom a perception system of the vehicle, the sensor data comprisinginformation about a position of at least one road reference in asurrounding environment of the vehicle; receive high-definition (HD) mapdata comprising a lane geometry of the surrounding environment of thevehicle, the lane geometry comprising a set of candidate lanes; form astate space model comprising a set of states, wherein each state of theset of states represents a candidate lane of the set of candidate lanes;define a cost for going from each state to every other state of the setof states based on the received vehicle data, the received sensor dataand the received HD map data; and determine a probable path of vehicletravel based on the formed state space model and the defined costs,wherein the control circuitry determines the probable path by decodingthe formed state space model in order to estimate the maximum likelihoodof a plurality of state sequences and determine the most probable statesequence; and send the probable path to a map generating system forupdating an HD map of the surrounding environment.
 10. The controldevice according to claim 9, wherein each state is a hidden state in aHidden Markov Model, and wherein the control circuit is configured todefine the cost based on: a first predefined probability E_(k) formaking an observation y_(k) at a time t_(k) when being in state x_(k),the state being represented by a candidate lane of said set of candidatelanes; and a second predefined probability T_(k) for moving from a firststate x_(k) of the set of states to another state x_(k+1) of the set ofstates at the time t_(k).
 11. The control device according to claim 10,wherein the sensor data comprises lane marker data comprising a distanceto the at least one lane marker and a lane marker type of the at leastone lane marker, wherein the vehicle data further comprises a yaw rateof the vehicle, wherein the first predefined probability Ek is based onat least one of the distance to the at least one lane marker, the lanemarker type of the at least one lane marker, the geographical positionof the vehicle, and the speed of the vehicle, wherein the secondpredefined probability Tk is based on the yaw rate of the vehicle. 12.The control device according to claim 10, wherein the control circuitryis further configured to: forming a spatial database based on the HD mapdata and a spatial indexing method.
 13. The control device according toclaim 9, wherein the control circuitry is further configured to: sendthe probable path to a control system for controlling adriver-assistance or autonomous driving feature of the vehicle based onthe probable path.
 14. A vehicle comprising: a perception systemcomprising at least one sensor for monitoring a surrounding environmentof the vehicle; an inertial measurement unit, IMU, for measuring aninertial movement of the vehicle; a localization system for monitoring ageographical position and a heading of the vehicle; and a control devicefor lane-level map matching for a vehicle, the control device comprisingcontrol circuitry configured to: receive vehicle data comprising ageographical position of the vehicle, a heading of the vehicle, and aspeed of the vehicle; receive sensor data from a perception system ofthe vehicle, the sensor data comprising information about a position ofat least one road reference in a surrounding environment of the vehicle;receive high-definition (HD) map data comprising a lane geometry of thesurrounding environment of the vehicle, the lane geometry comprising aset of candidate lanes; form a state space model comprising a set ofstates, wherein each state of the set of states represents a candidatelane of the set of candidate lanes; define a cost for going from eachstate to every other state of the set of states based on the receivedvehicle data, the received sensor data and the received HD map data; anddetermine a probable path of vehicle travel based on the formed statespace model and the defined costs, wherein the control circuitrydetermines the probable path by decoding the formed state space model inorder to estimate the maximum likelihood of a plurality of statesequences and determine the most probable state sequence; and send theprobable path to a map generating system for updating an HD map of thesurrounding environment.